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TDDet: A novel lightweight and efficient tea disease detector

文献类型： 外文期刊

作者： Sun, Yange 1 ; Li, Zhihao 1 ; Guo, Huaping 1 ; Feng, Yan 1 ; Tang, Yongqiang 3 ; Zhang, Wensheng 3 ; Gu, Jingqiu 4 ;

作者机构： 1.Xinyang Normal Univ, Sch Comp & Informat Technol, Xinyang 464000, Peoples R China

2.Xinyang Normal Univ, Henan Key Lab Tea Plant Biol, Xinyang 464000, Peoples R China

3.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： Tea disease; Object detection; Partial convolution; Efficient multiscale attention

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.9； 五年影响因子：9.3 ）

ISSN： 0168-1699

年卷期： 2025 年 237 卷

页码：

收录情况： SCI

摘要： Tea diseases cause significant economic losses to the tea industry every year, and thus developing a rapid and accurate tea disease detector is of great significance for assisting farmers in preventing diseases and increasing their income. Therefore, this paper proposes a lightweight and efficient detector called TDDet to quickly and accurately detect tea diseases. TDDet is mainly composed of two key innovations: feature extraction and feature aggregation. For feature extraction, we use lightweight depthwise separable convolution to reduce the computational load and enhance the ability to extract key local features in images of tea diseases. In addition, attention mechanisms including channel-, spatial-, and self-attentions, are employed to enable the model to focus on the most important parts of tea diseases, thereby improving the performance of the model. For feature aggregation, we propose a novel Cross-scale Feature Fusion (CFF) module to focus on tea disease areas, boosting the model's sensitivity to feature details. Based on CFF, TDDet repeatedly fuses multiscale features of different levels in a top-down and bottom-up manner, enhancing feature representation capability. Besides, a lightweight and efficient upsampling module, called Dysample, is used to reduce computational costs and improve model performance by dynamically adjusting the sampling rate of feature maps. Experimental results demonstrate that TDDet with fewer parameters outperforms other state-of-the-art object detection models, enabling fast and accurate identification of tea diseases. Our code and dataset are available at https://github.com/hpguo1982/TDDet.

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